JPH01164537A - Detection of tool anomaly by main spindle revolution speed change signal - Google Patents

Abstract

PURPOSE:To improve the reliability in the detection of tool anomaly by obtaining the pattern of the change of the cutting force applied onto a cutter from the variation signal of the main spindle revolution speed and judging the change from the normal pattern when an anomaly is generated in the pattern. CONSTITUTION:A pulse generator 1 is used for detecting the change of a main spindle revolving device, and the output pulse signal is changed to a voltage signal in a frequency/voltage converter 2. this signal is allowed to pass through a band-pass filter 3 for strengthening the pertinent frequency (number of revolution X number of cutters), and inputted into a calculator 5 by an analogue/ digital converter 4. In the calculator 5, the cutting state vector is obtained, and the distance from a standard vector is calculated, and anomaly judgement is performed. When an anomaly is detected, an alarm signal 7 is outputted for an NC controller 6. Further, in order to obtain the correspondence with an individual cutter on the tool and the revolution speed variation signal, the revolution pulse signal of a main spindle is obtained by using a photoelectric converter 8.

Description

[Detailed Description of the Invention] [Field of Industrial Application] The present invention is suitable for detecting tool failure in the field of cutting related to unmanned operation of machine tools, etc., particularly when interrupted cutting is performed. Regarding detection technology.

[Conventional technology]

As a conventional technology, there is a technology that detects fluctuations in the spindle motor current, performs signal processing using an autoregressive model, etc., and detects tool abnormalities.

[Problem that the invention seeks to solve]

In the conventional method of detecting spindle motor current, the signal contains many noise components other than those caused by fluctuations in cutting force, so complex signal processing is required and calculation time is required, and it is difficult to distinguish signs of abnormality from noise. There is a problem in that it is not easy to set a threshold for this purpose. Furthermore, included in the signal,
Due to ripples caused by the power supply, fluctuation components higher than the power supply frequency cannot be observed, and this method cannot be applied when the spindle rotation speed is high.

[Means for solving problems]

In order to solve the above-mentioned problems, the present invention obtains a change pattern of the cutting force applied to the cutting edge from a fluctuation signal of the spindle rotation speed, and determines whether this pattern changes from a normal pattern when an abnormality occurs. It is characterized by detecting tool abnormalities.

Detection of the spindle rotation speed can be performed, for example, using a speed generator or a pulse generator that is usually attached to the spindle motor to control the speed of the spindle motor. Examples of signals obtained when using a six-blade face milling tool are shown in FIGS. 4A and 4B. Fig. 4A shows the case where a normal tool is used, and Fig. 4B shows the case where one of the cutting edges is damaged. As is clear from FIG. 4A, the signal clearly shows fluctuations in the cutting force caused by the intermittent cutting of each cutting blade. Also, this variation is caused by the spindle 1
The same pattern is repeated with each rotation as a period. However, in the case of the defective tool shown in FIG. 4B, this pattern has changed significantly.

The characteristics of the change pattern of the spindle rotational speed fluctuation signal for one rotation of the spindle are extracted in the form of a pattern vector. for example,
As shown in FIG. 3, the maximum and minimum values of fluctuation for one rotation of the main shaft are determined and used as a pattern vector. The number of cutting edges is m1, and the first maximum value and minimum value during one rotation of the spindle are H(i) and L(
i) If (i = L..., m), the pattern vector P is P = (H(1),..., H(m),
L(1), . . . , L(m>). However, if left as is, the pattern vector changes depending on the cutting conditions. Therefore, standardization is performed so that even if the cutting conditions change, the pattern vector remains constant as long as the state of the cutting edge does not change. That is, the following conversion is performed on the local maximum value H(]).

When similar normalization is performed for L(1), the normalized vector p'=ct+'c, ..., H' (e), L
'(1), ..., L' (e)] is found. This is called a cutting state vector.

To detect a tool abnormality, cutting is performed in advance under standard cutting conditions, and a cutting state vector when the tool is in a normal state is determined as a reference vector. During actual work, the distance between the cutting state vector P', which is calculated from the observed signals from time to time, and the reference vector p/, is determined as follows.

When this distance exceeds a certain threshold value, it is determined that a tool abnormality has occurred.

Note that the detection algorithm shown here is just an example, and there are other ways to identify changes in the pattern of the spindle rotational speed signal due to tool abnormalities, such as using variations in the amplitude value or amplitude distribution of the fluctuation signal. There are various things that can be considered.

[For production]

The fluctuation signal of the spindle rotation speed reflects changes in the cutting resistance applied to the cutting tool well and has little noise. Therefore, it is possible to clearly identify a change in the cutting process due to an abnormality in the cutting tool, and a resulting change in the cutting resistance variation pattern from the spindle rotational speed variation signal. For this reason, even initial abnormalities such as minute tool defects can be detected with high sensitivity and high reliability without being overly influenced by threshold settings. In addition, since there is little noise, relatively simple detection algorithms such as pattern matching can be applied.
The calculation load is light.

Furthermore, since the signal does not include ripples caused by the power supply, the detectable spindle rotational speed range is not limited. Therefore, in addition to requiring less calculation time for abnormality determination, this method can be applied to cases where the spindle rotation speed is several thousand rpm.

〔Effect of the invention〕

According to the present invention, it is possible to provide a tool abnormality monitoring function to an automated production system, and in particular, in a system characterized by unmanned operation, it is possible to sensitively detect tool breakage that occurs suddenly and cannot be predicted. In realizing unmanned driving,
It has a big effect.

Further, since the fluctuation signal of the spindle rotational speed does not require a special measuring device that would get in the way of work, it has the effect of realizing a tool abnormality detection system that is inexpensive, simple, and highly reliable.

〔Example〕

An example of the configuration of a tool abnormality detection system using the present invention is shown in FIG. A pulse generator 1 is used to detect fluctuations in the spindle rotating device. In this case, the pulse signal output therefrom is converted into a voltage signal by the frequency/voltage converter 2. A speed generator may be used instead of the pulse generator 1. In that case, a direct voltage signal is obtained. The signal is passed through a band pass filter 3 to emphasize the frequency of interest (number of rotations x number of blades), and then inputted into a computer 5 from an analog/digital converter 4. The computer 5 obtains the cutting state vector, calculates the distance from the reference vector, and determines whether there is an abnormality. If an abnormality is detected, an alarm signal 7 is output to the numerical control device 6.

Note that a photoelectric converter 8 is used to obtain the spindle rotation bus signal in order to correspond to the rotational speed fluctuation signal and each cutting edge on the tool.

Figure 2 shows an example of detecting a missing tool using this system. The tool used was a four-blade face milling tool with a tool diameter of 80 mm, and the work material was structural carbon steel 545C. This figure shows the distance between the cutting state vector obtained at each point in time and the reference vector. At the point indicated by the arrow, one cutting edge is damaged.

Correspondingly, it can be seen that the distance increases rapidly, clearly indicating the occurrence of tool abnormality.

[Brief explanation of the drawing]

Fig. 1 is a configuration diagram of a tool abnormality detection system showing an embodiment of the present invention, Fig. 2 is a graph showing an example of tool defect detection when the present invention is implemented, and Fig. 3 is a rotation of one spindle indicated by a defective tool. Figures 4A and 4B are graphs of spindle rotational speed variation waveforms shown by a normal tool and a defective tool, respectively. 1... Pulse generator, 2... Frequency/voltage converter, 3... Band pass filter, 4... Analog/digital converter, 5... Calculator, 6... ...Numerical control device, 7..Alarm signal, 8..Photoelectric detector. District 7

Claims (1)

[Claims] A method for early detection of abnormalities in cutting tools based on fluctuation patterns in the spindle speed of machine tools.